In some retail contexts, stocking large quantities of inventory may not only improve service levels, but can also stimulate demand. For products having demand rates that increase with inventory levels, we analyze the effect of stocking decisions on firm profitability to develop managerial insights regarding the structure of the optimal inventory policy, and to understand how this policy differs from traditional approaches. When inventories stimulate demand, iteratively applying the standard economic order quantity (EOQ) model–by setting the demand rate parameter equal to the observed average demand rate in prior cycles–yields an equilibrium order quantity that is robust to demand parameter misestimation, but is suboptimal. The profit-maximizing policy orders larger quantities, and can replenish inventory even before on-hand stock fully depletes. Using an extension of a standard inventory-dependent demand model from the literature, we first provide a convenient characterization of products that require early replenishment. We demonstrate that the optimal cycle time is largely governed by the conventional trade-off between ordering and holding costs, whereas the reorder point relates to a promotions-oriented cost-benefit perspective. We show that the optimal policy yields significantly higher profits than cost-based inventory policies, underscoring the importance of profit-driven inventory management.

Effective distribution using collaborative fulfillment networks requires coordination among the multiple participating firms at different stages of the supply chain. Acting independently, supply chain partners fail to weigh the cost burden they impose on upstream suppliers when their replenishment order quantities vary from period to period. This paper explores a new approach to coordinate multiple stages in the supply chain by controlling, through appropriate downstream inventory management, the demand variability that is propagated to upstream stages. We propose and analyze a coordinated inventory replenishment policy that uses "order smoothing" to reduce order-size variability and thus reduce overall system costs, including both inventory and transportation costs. We characterize the optimal parameter values for smoothing alternatives (such as exponential smoothing and moving weighted average policies), assess their economic benefits, and develop insights regarding supply chain contexts that might benefit most significantly from reducing the variability of orders to upstream stages. Using the distribution network for specialty brand appliances as an illustrative example, we demonstrate the potential cost savings that order-smoothing strategies can yield compared to the uncoordinated case when individual firms separately minimize their costs. The magnitude of savings depends on several factors, including the variability in consumer demand, level of product variety, and degree of inventory aggregation in the distribution system. Based on our analytical results, we develop a framework to assess cost reduction opportunities through variability control for different supply chain scenarios.supply chain coordination, inventory control, variability reduction, distribution systems, collaborative fulfillment, order smoothing

In some retail contexts, higher inventories not only improve service levels, but also stimulate demand by serving as a promotional tool (e.g., by increasing product visibility). Motivated by a building-products retailer's practice of stocking large quantities of products to stimulate demand, we study inventory management and pricing policies when demand is uncertain but increases with stocking quantity. We first characterize the profit-maximization policy for a stochastic inventory model with a general inventory-dependent demand distribution and given price, and show that demand stimulation (by inventories) has the effect of increasing the target service level beyond the classical newsvendor model's critical fractile ratio. To underscore the importance of considering both demand stochasticity and inventory influence, we consider two functionally oriented benchmark policies--a demand-driven policy and a critical fractile policy--that might, respectively, represent marketing and inventory managers' viewpoints. Our numerical analysis reveals that the optimal policy can generate considerably higher profits than the two complementary functional perspectives. Moreover, we prove that the optimal stocking quantity always exceeds the critical fractile solution and can even exceed the demand-driven stocking quantity. We also address the problem of jointly optimizing both stocking quantity and price for demand-stimulating products using a multiplicative model to represent the influence of price and stocking quantity on the demand distribution. For this model, we show that the pricing and stocking decisions can be determined sequentially, with the optimal policy setting higher prices and stock levels than both the functional policies.retail operations, inventory management, pricing, newsvendor model, promotional inventories

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